Multi-view multi-label data offers richer perspectives for artificial intelligence, but simultaneously presents significant challenges for feature selection due to the inherent complexity of interrelations among features, views and labels. Attention mechanisms provide an effective way for analyzing these intricate relationships. They can compute importance weights for information by aggregating correlations between Query and Key matrices to focus on pertinent values. However, existing attention-based feature selection methods predominantly focus on intra-view relationships, neglecting the complementarity of inter-view features and the critical feature-label correlations. Moreover, they often fail to account for feature redundancy, potentially leading to suboptimal feature subsets. To overcome these limitations, we propose a novel method based on Redundancy-optimized Multi-head Attention Networks for Multi-view Multi-label Feature Selection (RMAN-MMFS). Specifically, we employ each individual attention head to model intra-view feature relationships and use the cross-attention mechanisms between different heads to capture inter-view feature complementarity. Furthermore, we design static and dynamic feature redundancy terms: the static term mitigates redundancy within each view, while the dynamic term explicitly models redundancy between unselected and selected features across the entire selection process, thereby promoting feature compactness. Comprehensive evaluations on six real-world datasets, compared against six multi-view multi-label feature selection methods, demonstrate the superior performance of the proposed method.

翻译：多视图多标签数据为人工智能提供了更丰富的视角，但由于特征、视图和标签之间内在关联的复杂性，同时也给特征选择带来了显著挑战。注意力机制为分析这些复杂关系提供了一种有效途径，其通过聚合查询矩阵与键矩阵之间的相关性来计算信息的重要性权重，从而聚焦于相关数值。然而，现有的基于注意力的特征选择方法主要关注视图内部关系，忽视了视图间特征的互补性以及关键的特征-标签相关性。此外，这些方法往往未能考虑特征冗余，可能导致所选特征子集非最优。为克服这些局限，本文提出一种基于冗余优化多头注意力网络的多视图多标签特征选择新方法（RMAN-MMFS）。具体而言，我们利用每个独立的注意力头建模视图内特征关系，并通过不同头之间的交叉注意力机制捕获视图间特征互补性。进一步地，我们设计了静态与动态特征冗余项：静态项用于减少各视图内的冗余，而动态项则在整体选择过程中显式建模未选特征与已选特征间的冗余，从而提升特征紧凑性。在六个真实数据集上，与六种多视图多标签特征选择方法的综合对比实验表明，所提方法具有优越性能。